Nearest neighbor classification can be used to solve real world problems such as determining whether a handwritten number is even or odd, or whether a handwritten marking is a letter, a number, a lower case letter, an uppercase letter, or other symbol. Conventional computation methods for performing such classifications tend to require large numbers of processing steps. Quantum computing methods can permit more rapid solution of some conventional computational problems such as searching and factorization. Quantum computing methods for classification have been based on mean data values. In many practical examples, mean data values are not suitable, especially if data values have irregular or complex distributions. For example, in many practical applications, success probabilities of only about 50% are obtained with mean value based methods, rendering such methods no more reliable than flipping a coin. Improved methods for classification using quantum computers are needed.